Supposing I have:
some individuals a1 and a2 from class A, b1 and b2 from class B
an object property "myProperty" from A to A or B to B.
an object property "otherProperty" from A to B
a1 :myProperty a2
a1 :otherProperty b1 and a2 :otherProperty b2
Is there a simple way to infer b1 :myProperty b2?
I am using Protégé with OWL, and I haven't found in the documentation (https://protegeproject.github.io/protege/views/object-property-characteristics) any way to do it.
Related
I have some nested models with foreign key relationships going 4 levels deep.
A <- B <- C <- D
A has a set of B models, which each have a set of C models, which each have a set of D models.
I'm iterating of the each model (4 layers of looping from A down to B). This is producing lots of DB hits.
I don't need to do any filtering at the DB fetch level, as I need all the data from all the DB tables, so I ideally I'd like to get all the data with ONE SQL query that hits the DB (if that's possible) and then somehow have the data organized/filtered into their correct sets for each model. i.e. it's all pre-fetched and structured ready for using the data (e.g. in a web dashboard).
There seems to be a lot of django related pre-fetch helpers and packages, but none of them seem to work the way I expect. e.g. django-auto-prefetch (which seems ideal).
Is this a common use case (I thought it would be)?
How can I construct the ORM to get all the data in one hit and then just use the bits I need.
NOTE: target system is raspberry pi class device (1GHz Arm processor) with eMMC storage (similar to SD card), and using SQLite as the DB backend.
NOTE: I'm also using this with django-polymorphic, which may or may not make a difference?
Thanks, Brendan.
Using one query would result in a huge amount of bandwidth, since the values for the columns of the A model will be repeated per B model per C model per D model.
Indeed, the response would look like:
a_col1 | a_col2 | b_col1 | b_col2 | c_col1 | d_col1
A1 A1 B1 B1 C1 D1
A1 A1 B1 B1 C1 D2
A1 A1 B1 B1 C1 D3
A1 A1 B1 B1 C2 D4
A1 A1 B1 B1 C2 D5
A1 A1 B1 B1 C2 D6
A1 A1 B2 B2 C3 D7
A1 A1 B2 B2 C3 D8
A1 A1 B2 B2 C3 D9
A1 A1 B2 B2 C4 D10
A1 A1 B2 B2 C4 D11
A1 A1 B2 B2 C4 D12
A2 A2 B3 B3 C5 D13
A2 A2 B3 B3 C5 D14
A2 A2 B3 B3 C5 D15
A2 A2 B3 B3 C5 D16
We thus would repeat the values for the a_columns, b_columns, etc. a large number of times resulting in a large amount of bandwidth going from the database to the Python/Django layer. This would not only result in large amounts of data being transferred, but also large amounts of memory being used by Django to deserialize this response.
Therefore .prefetch_related makes one (or two depending on the type of relation) extra queries per level at most, so three to seven queries in total, which will minimize the bandwidth.
You thus can fetch all objects in memory with:
for a in A.objects.prefetch_related('b_set', 'b_set__c_set', , 'b_set__c_set__d_set'):
print(a)
for b in a.b_set.all():
print(b)
for c in b.c_set.all():
print(c)
for d in c.d_set.all():
print(d)
Is there any formulaic way of recursively working through one column and combining it with values from another? For illustration, given...
Column A:
A
B
C
and
Column B:
1
2
3
can I generate...
Column C:
A1
A2
A3
B1
B2
B3
C1
C2
C3
try:
=ARRAYFORMULA(SORT(
TRANSPOSE(SPLIT(REPT(CONCATENATE(A1:A&CHAR(9)), COUNTA(B1:B)), CHAR(9)))&
TRANSPOSE(SPLIT(CONCATENATE(REPT(B1:B&CHAR(9), COUNTA(A1:A))), CHAR(9)))))
I need to generate possible ranking of all possible ranking of n documents. I understand that the permutations of an array {1, 2,..., n} will give me the set of all possible rankings.
My problem is a bit more complex as each document could take one of 2 possible types. Therefore, in all there are n!*2n possible rankings.
For instance, let us say I have 3 documents a, b, and c. Then possible rankings are the following:
a1 b1 c1
a1 b1 c2
a1 b2 c1
a1 b2 c2
a2 b1 c1
a2 b1 c2
a2 b2 c1
a2 b2 c2
a1 c1 b1
a1 c1 b2
a1 c2 b1
a1 c2 b2
a2 c1 b1
a2 c1 b2
a2 c2 b1
a2 c2 b2
b1 a1 c1
b1 a1 c2
b1 a2 c1
b1 a2 c2
b2 a1 c1
b2 a1 c2
...
What would be an elegant way to generate such rankings?
It's a kind of cross product between the permutations of B={a,b, ...} and the k-combinations of T{1,2} where k is the the number of elements in B. Say we take a p from Perm(B), e.g. p=(b,c,a) and a c from 3-Comb(T), e.g. c=(2,1,1) then we would merge p and c into (b2,c1,a1).
I don't really know if it's elegant but I would choose an algorithm to generate sequentially the permutations of B (cf TAOCP Volume 4 fascicle 2b) and for each permutation apply the above "product" with all the k-combinations generated sequentially (or stored in an array if k is small) (cf TAOCP Volume 4 fascicle 3a).
B={a,b,c, ... }
T={1,2}
k=length(B)
reset_perm(B)
do
p = next_perm(B)
reset_comb(T,k)
do
c = next_kcomb(T,k)
output product(p,c)
while not last_kcomb(T,k)
while not last_perm(B)
By counting in facet results I mean resolve the problem:
I have 7 documents:
A1 B1 C1
A2 B1 C1
A3 B2 C1
A4 B2 C2
A5 B3 C2
A6 B3 C2
A7 B3 C2
If I make the facet query by field B, get the result: B1=2, B2=2, B3=3.
A1 B1 C1
A2 B1 C1 2 - facing by B
--------------====
A3 B2 C1
A4 B2 C2 2 - facing by B
--------------====
A5 B3 C2
A6 B3 C2
A7 B3 C2 3 - facing by B
--------------====
I want to get additional information, something like count in results, by field C. So, how can I query to get a result similar to the following:
A1 B1 C1
A2 B1 C1 2, 1 - facing by B, count C in facet results
--------------=======
A3 B2 C1
A4 B2 C2 2, 2 - facing by B, count C in facet results
--------------=======
A5 B3 C2
A6 B3 C2
A7 B3 C2 2, 1 - facing by B, count C in facet results
--------------=======
Thanks
What you need is Facet Pivots
This will help you get the results and counts of hierarchies.
This is available in Solr 4.0 trunk build. So you may need to apply the patch.
References -
http://wiki.apache.org/solr/HierarchicalFaceting
http://wiki.apache.org/solr/SimpleFacetParameters#Pivot_.28ie_Decision_Tree.29_Faceting
Is there a way to loop over multiple lists in parallel in a makefile or CMake file?
I would like to do something like the following in CMake, except AFAICT this syntax isn't supported:
set(a_values a0 a1 a2)
set(b_values b0 b1 b2)
foreach(a in a_values b in b_values)
do_something_with(a b)
endforeach(a b)
This would execute:
do_something_with(a0 b0)
do_something_with(a1 b1)
do_something_with(a2 b2)
I would accept an answer in either CMake or Make, though CMake would be preferred. Thanks!
Here you go:
set(list1 1 2 3 4 5)
set(list2 6 7 8 9 0)
list(LENGTH list1 len1)
math(EXPR len2 "${len1} - 1")
foreach(val RANGE ${len2})
list(GET list1 ${val} val1)
list(GET list2 ${val} val2)
message(STATUS "${val1} ${val2}")
endforeach()
As of CMake 3.17, the foreach() loop supports a ZIP_LISTS option to iterate through two (or more) lists simultaneously:
set(a_values a0 a1 a2)
set(b_values b0 b1 b2)
foreach(a b IN ZIP_LISTS a_values b_values)
message("${a} ${b}")
endforeach()
This prints:
a0 b0
a1 b1
a2 b2
In make you can use the GNUmake table toolkit to achieve this by handling the two lists as 1-column tables:
include gmtt/gmtt.mk
# declare the lists as tables with one column
list_a := 1 a0 a1 a2 a3 a4 a5
list_b := 1 b0 b1 b2
# note: list_b is shorter and will be filled up with a default value
joined_list := $(call join-tbl,$(list_a),$(list_b), /*nil*/)
$(info $(joined_list))
# Apply a function (simply output "<tuple(,)>") on each table row, i.e. tuple
$(info $(call map-tbl,$(joined_list),<tuple($$1,$$2)>))
Output:
2 a0 b0 a1 b1 a2 b2 a3 /*nil*/ a4 /*nil*/ a5 /*nil*/
<tuple(a0,b0)><tuple(a1,b1)><tuple(a2,b2)><tuple(a3,/*nil*/)><tuple(a4,/*nil*/)><tuple(a5,/*nil*/)>